Method for reconstructing incomplete data of X-ray absorption contrast computed tomography based on deep learning

A method for reconstructing incomplete data of X-ray absorption contrast computed tomography (CT) based on deep learning (DL), the method comprising:

• a step 1 of using a filtered back projection (FBP) algorithm to reconstruct incomplete absorption contrast projection sequences obtained by an X-ray absorption contrast CT system to obtain an initial reconstructed image, wherein the initial reconstruction image in an initial reconstruction result of FBP comprises artifacts and noise due to the incomplete projection sequences;
• a step 2 of using a forward projection operator to forward project the initial reconstructed image to obtain artifact-contaminated complete projection sequences, wherein the artifact-contaminated complete projection sequences are obtained by forward projecting image structure information and the artifacts into the projection sequences;
• a number of the sequences satisfies a complete condition, that is, a Nyquist sampling theorem;
• a step 3 of using a DL technique to process the artifact-contaminated complete projection sequences to obtain artifact-free complete projection sequences, wherein the complete projection sequences obtained by processing the artifact-contaminated complete projection sequences by using the DL technique do not comprise the artifacts;
• a number of the sequences satisfies a complete condition;
• and
• a step 4 of using the FBP algorithm to reconstruct the artifact-free complete projection sequences to obtain a final reconstructed image.